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Multilevel Modelling of Medical Data
, 2001
"... This tutorial presents an overview of multilevel or hierarchical data modelling and its applications in medicine. A description of the basic model for nested data is given and it is shown how this can be extended to fit flexible models for repeated measures data and more complex structures involving ..."
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Cited by 25 (0 self)
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This tutorial presents an overview of multilevel or hierarchical data modelling and its applications in medicine. A description of the basic model for nested data is given and it is shown how this can be extended to fit flexible models for repeated measures data and more complex structures involving cross classifications and multiple membership patterns within the software package MLwiN. A variety of response types is covered and both frequentist and Bayesian estimation methods are described.
H (2006) Crossclassified and multiple membership structures in models: an introduction and review
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Parallel implementation of a multilevel modelling package
 COMPUTATIONAL STATISTICS AND DATA ANALYSIS
, 1999
"... A portable parallel implementation of MLn, a multilevel modelling package, for shared memory parallel machines is described. Particular attention is paid to crossclassified and multiple membership models, which are more computationally demanding than those with simple hierarchical structure. Perfor ..."
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Cited by 8 (2 self)
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A portable parallel implementation of MLn, a multilevel modelling package, for shared memory parallel machines is described. Particular attention is paid to crossclassified and multiple membership models, which are more computationally demanding than those with simple hierarchical structure. Performance results are presented for a range of shared memory parallel architectures, demonstrating a significant increase in the size of models which can be handled interactively.
Multilevel models in the study of dynamic household structures
 European Journal of Population
, 2000
"... Abstract. A modelling procedure is proposed for complex, dynamic household data structures where households change composition over time. Multilevel multiple membership models are presented for such data and their application is discussed with an example. ..."
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Cited by 8 (5 self)
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Abstract. A modelling procedure is proposed for complex, dynamic household data structures where households change composition over time. Multilevel multiple membership models are presented for such data and their application is discussed with an example.
Methods in school effectiveness research. School effectiveness and school improvement
, 1997
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Ordered category responses and random effects in multilevel and other complex structures: scored and generalized linear models
 In S. P. Reise and N. Duan (eds) Multilevel Modeling: Methodological Advances, Issues, and Applications. Mahwah, NJ: Erlbaum
, 2002
"... Many measured characteristics of observational units that are the subject of statistical analysis in empirical research are categorisations. They are represented in data as one of a limited set of mutually exclusive and collectively exhaustive set of labels. Often operational definition places the l ..."
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Cited by 6 (4 self)
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Many measured characteristics of observational units that are the subject of statistical analysis in empirical research are categorisations. They are represented in data as one of a limited set of mutually exclusive and collectively exhaustive set of labels. Often operational definition places the labels into a meaningful unambiguous order. Ordered categories ensue. Sometimes consecutive integer numbers are attached to category labels in coding. However, it is only the order relationship amongst these numbers that have any substantive meaning. Indeed, any set of numbers may be applied as labels providing it is only the monotonic ordering of the number values that is construed as having any such meaning. Thus as they stand the explicit numbers possess minimal properties. These make them very low in the hierarchy of measurement scale levels expounded in standard literature (e.g. Torgerson,1958). For example, without further qualification, distances between the numbers have no relevant interpretation. For instance, using consecutive integers, an observation of unity is lower than another measured
GPvam: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in ValueAdded Modeling, http://cran.rproject.org/web/packages/GPvam/index.html, R package version
, 2012
"... Abstract. The generalized persistence (GP) model, developed in the context of estimating “value added ” by individual teachers to their students ’ current and future test scores, is one of the most flexible valueadded models in the literature. Although developed in the educational setting, the GP m ..."
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Cited by 4 (4 self)
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Abstract. The generalized persistence (GP) model, developed in the context of estimating “value added ” by individual teachers to their students ’ current and future test scores, is one of the most flexible valueadded models in the literature. Although developed in the educational setting, the GP model can potentially be applied to any structure where each sequential response of a lowerlevel unit may be associated with a different higherlevel unit, and the effects of the higherlevel units may persist over time. The flexibility of the GP model, however, and its multiple membership random effects structure lead to computational challenges that have limited the model’s availability. We develop an EM algorithm to compute maximum likelihood estimates efficiently for the GP model, making use of the sparse structure of the random effects and error covariance matrices. The algorithm is implemented in the package GPvam in R statistical software. We give examples of the computations and illustrate the gains in computational efficiency achieved by our estimation procedure. NOTICE This is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [VOL59, March, (2013)] DOI:10.1016/j.csda.2012.10.004 1.
TUTORIAL IN BIOSTATISTICS Multilevel modelling of medical data
, 2001
"... This tutorial presents an overview of multilevel or hierarchical data modelling and its applications in medicine. A description of the basic model for nested data is given and it is shown how this can be extended to t exible models for repeated measures data and more complex structures involving cro ..."
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Cited by 4 (0 self)
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This tutorial presents an overview of multilevel or hierarchical data modelling and its applications in medicine. A description of the basic model for nested data is given and it is shown how this can be extended to t exible models for repeated measures data and more complex structures involving crossclassications and multiple membership patterns within the software package MLwiN. A variety of response types are covered and both frequentist and Bayesian estimation methods are described. Copyright? 2002 John Wiley & Sons, Ltd.
Decomposition of prediction error in multilevel models
, 2002
"... We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y∗j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study t ..."
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Cited by 3 (1 self)
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We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y∗j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study that spans the various combinations of level1 (individual) and level2 (group) sample sizes and different intraclass correlation values. Additionally, analytical results present the increase in predicted mean square error (PMSE) with respect to prediction error bias. The components of prediction error provide information with respect to the cost of parameter estimation versus data imputation for predicting future values in a hierarchical data set. Specifically, the cost of parameter estimation is very small compared to data imputation.